Hi there! I’m excited to explore a topic that’s sparked my curiosity: artificial intelligence (AI). In recent years, AI has changed from a sci-fi dream into something I use every day. Whether I’m chatting with a language model or admiring generated art, it’s everywhere. As someone who loves building, I’ve worked hard to understand AI and create my own apps. Today, I want to share the books that have influenced my journey. These choices aren’t random. They are resources that have filled gaps in my knowledge. They’ve helped me code and encouraged me to think critically about AI’s future.
In this article, we’ll look at eight amazing books. Three focus on my experiences with large language models (LLMs) and AI engineering. The other five cover the wider AI landscape. I’ll explain what each book has to offer, why it’s excellent and how it has influenced how I grow as an AI enthusiast. I have something for every level of coder, from beginners to experts. Let’s get started.
Why Books? My Love for Learning Through Pages
Before we get to the good stuff, let me tell you why I’m so hooked on books for learning AI. In a world full of flashy tutorials and quick YouTube clips, books provide something unique: depth. They force me to slow down, think, and wrestle with concepts until they click. I also love flipping through pages or scrolling a PDF. It feels great to know I’m building a solid foundation. AI is growing quickly—sometimes faster than I can keep up. But these books help me stay grounded and keep trying new things.
One observation I’ve made is that recent AI books tend to obsess over LLMs, like the ones powering ChatGPT. And sure, LLMs are mind-blowing, but they’re not the whole story. Yann LeCun, a leader in deep learning, thinks LLMs can’t reason, plan, or achieve artificial general intelligence (AGI). That’s a bold take, and it’s got me thinking about the bigger picture. LLMs are among the best tools we have today, and they’re changing how I tackle projects. So, let’s start with the books that helped me master them.
Diving Deep into Large Language Models
Build a Large Language Model (From Scratch) by Sebastian Raschka
What It’s About
The first book on my list is Build a Large Language Model (From Scratch) by Sebastian Raschka. This guide is ambitious and hands-on. It takes me step by step through coding an LLM, like GPT-2. If you’ve ever wondered what’s inside the “black box” of an LLM, this book is like a treasure map. It’s the most detailed resource I’ve found for understanding these models at their core.
Why I Love It
You might be thinking, “Why build my own LLM when I can just use GPT-4?” I asked myself the same thing! But here’s the deal: there’s no better way to truly get how these models work than by creating one myself. Physicist Richard Feynman once said, “What I cannot create, I do not understand.” This book embodies that idea. Building an LLM from scratch gave me a basic understanding that API calls can’t match.
Plus, there are practical perks. In fields like finance or medicine, a custom-built model can outperform general-purpose ones. If I worry about data privacy (which I often do), training my own LLM keeps everything in-house. This means I don’t share sensitive info with third-party companies like OpenAI.
What I Learned
This book covers both theory and implementation, and I mean everything:
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Transformers and Attention: The backbone of modern LLMs. I finally grasped how the attention mechanism lets models focus on what matters in a sentence.
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Pre-training and Fine-tuning: I learned how to pre-train a model on raw data and tweak it with instruction datasets for specific tasks.
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Python and PyTorch: The book uses PyTorch, a deep learning library I’ve come to adore. There’s even an appendix to get me up to speed if I’m rusty.
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Visuals That Click: As a visual learner, I was obsessed with the diagrams. They made complex ideas—like multi-layered architectures—feel digestible.
The code is all available in a public GitHub repo, which I’ve been tinkering with nonstop. It assumes I’ve got basic-to-intermediate Python skills, so if you’re new to coding, I’d recommend brushing up first. (Shameless plug: I’ve got a Python for AI course linked below if you need a jumpstart!)
Who It’s For
If you’re a machine learning researcher, an AI engineer, or just eager to understand LLMs, this book is a gem. It’s been a game-changer for my projects, and I can’t recommend it enough.
AI Engineering: Building Applications with Foundation Models by Chip Huyen
What It’s About
Next up is AI Engineering: Building Applications with Foundation Models by Chip Huyen. This book came at the perfect time for me, especially with all the hype around models like DeepSeek v1. It’s a full guide on using foundation models like LLMs and large multimodal models (LMMs) in real-life situations. Yann LeCun might say LLMs won’t lead to AGI. But Chip Huyen shows how they’re opening up huge opportunities.
Why I Love It
I spend a lot of my time dreaming up AI applications—ways to solve problems or build something new. This book nails that mindset. It’s not about making new models. It’s about using what’s already available to build strong, dependable solutions. Many people can create a simple AI app if they have some coding skills. But truly grasping the technology? That’s a different story. That’s where the magic happens.
The rise of AI engineering as a discipline fascinates me. Open-source AI frameworks are growing faster than traditional tools like ReactJS or Bitcoin in its early days. This book captures that shift and gives me a roadmap to ride the wave.
What I Learned
Here’s a taste of what I picked up:
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Foundation Models 101: A look at today’s models and their uses—coding, image generation, chatbots, and more.
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Practical Considerations: Before I build anything, I now ask: Is AI even the right solution? Sometimes a simple regression model beats an LLM, and I’ve learned to avoid the FOMO trap.
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Technical Deep Dive: I see how these models evolve. They begin with unsupervised pre-training. Then, they apply reinforcement learning with human feedback (RLHF).
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Scaling Laws: Bigger models usually need more data and computing power. However, models like DeepSeek v1 challenge this idea. It’s got me rethinking assumptions.
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Adaptation Techniques: My toolbox includes prompt engineering, retrieval-augmented generation (RAG), and fine-tuning methods. These help me adjust models to fit my needs.
The GitHub repo for this book is packed with summaries and resources, which I’ve been digging into. Some parts seemed complex when I was new to deep learning. However, Chip Huyen explains things simply, so I could still keep up.
Who It’s For
If you’re working at a company integrating AI—or just want to build something impactful—this book is a must-read. The fundamentals it teaches will stay relevant even as specific stats age. I’ve learned a ton, and it’s shaped how I approach my projects.
LLM Engineer’s Handbook: Master the Art of Engineering Large Language Models from Concept to Production by Paul Iacono and Maximo Labanca
What It’s About
The third book in my LLM trilogy is LLM Engineer’s Handbook by Paul Iacono and Maximo Labanca. This one’s for the engineers among us (hi, that’s me!) who want to build, optimize, and deploy LLM-based apps. It’s less about theory and more about getting my hands dirty with code and architecture.
Why I Love It
This book is so hands-on. The authors often use an example: creating an “LM Twin.” This is a digital version of me that copies my writing style. It’s not the most groundbreaking idea, but it’s a concrete case study I could adapt to my own projects. I loved seeing the full pipeline, from data collection to cloud deployment.
What I Learned
Here’s what I’ve been playing with:
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Pipeline Breakdown:
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Data engineering
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Supervised fine-tuning
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RAG
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Evaluation
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Deployment
All steps are laid out clearly.
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LM Twin Project: I used my own social media posts to train a model that writes like me. It’s eerie but cool!
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LM Ops: I explored more than just traditional MLOps. I learned about prompt monitoring, guardrails, and human-in-the-loop feedback.
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Tools Galore: ZenML, Docker, AWS SageMaker—the book introduced me to a toolkit I’m still exploring.
The downside? Tools evolve fast, and some might not fit my company’s setup. But the concepts are timeless, and the free GitHub repo is a bonus.
Who It’s For
If you’re technically inclined and want a practical guide to LLM development, this is your book. It’s been a blast to work through, and I’m already deploying my first app inspired by it.
Broadening My AI Horizons
Now, let’s switch gears. The next five books came from another source that widened my perspective beyond LLMs. These include generative AI, natural language processing (NLP), and basic machine learning. They’re great for enhancing my skills.
Generative AI: Comprehensive Guide for Beginners
What It’s About
Generative AI: Comprehensive Guide for Beginners is my go-to recommendation for anyone new to AI. It’s all about how generative AI—like ChatGPT—can power business growth and personal projects, even if I’m just starting out.
Why I Love It
Imagine sitting back while AI churns out content or cash—this book makes that feel possible. It’s a roadmap to understanding generative AI’s potential, and it’s broken down so simply that I didn’t feel overwhelmed as a beginner.
What I Learned
Here’s what I took away:
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Content Creation: Using ChatGPT to whip up blog posts and social media updates effortlessly.
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Digital Products: Turning my expertise into eBooks or courses for passive income.
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Customer Support: Letting AI handle queries 24/7.
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Market Research: Crafting strategies with AI insights.
Who It’s For
If you’re dipping your toes into AI and want a practical starting point, this is it. It’s been a confidence booster for me as I explore what’s possible.
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster
What It’s About
This one’s a bit more technical: Generative Deep Learning by David Foster. It’s for folks like me who know some machine learning and want to get creative—think AI that paints, writes music, or generates images.
Why I Love It
I’m a sucker for practical examples, and this book delivers. It’s well-written, with tips and tricks that unlocked my models’ creative potential. Even as a semi-beginner, I could follow along thanks to its clarity.
What I Learned
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Variational Autoencoders (VAEs): Transforming facial expressions in photos.
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Generative Adversarial Networks (GANs): Creating custom images with my own datasets.
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Diffusion Models: Generating new flower varieties—how cool is that?
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Text and Music: Building my own text generator and composing polyphonic tunes.
Who It’s For
If you’ve got some machine learning chops and want to push AI creatively, this book’s a gem. It’s stretched my imagination in the best way.
Quick Start Guide to Large Language Models
What It’s About
The Quick Start Guide to Large Language Models is my shortcut to mastering LLMs, even as a newbie. It’s packed with hands-on exercises and real-world examples to get me building fast.
Why I Love It
LLMs can feel intimidating, but this book breaks them down into bite-sized pieces. I felt like a pro in no time, thanks to its insider tips and clear explanations.
What I Learned
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Core Concepts: Pre-training, fine-tuning, attention—demystified.
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Prompt Engineering: Mastering advanced techniques to get the most out of LLMs.
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Customization: Building recommendation engines with user data.
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Deployment: Scaling my creations to the cloud.
Who It’s For
New to LLMs? This is your fast track. It’s been a lifeline for me as I tackle bigger projects.
Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
What It’s About
For the tech pros out there, Natural Language Processing with Transformers is a deep dive into Transformers and LLMs. It’s a how-to guide for putting these models to work on my projects.
Why I Love It
The clarity of the explanations blew me away, and the readable code made it a joy to experiment with. It strikes a perfect balance between theory and practice.
What I Learned
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NLP Tasks: Text classification, question answering, and more.
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Efficiency Tricks: Distillation, pruning, quantization—I’m obsessed.
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Training from Scratch: Building Transformers on multiple GPUs.
Who It’s For
Data scientists and engineers with Python and deep learning experience—this one’s for you. It’s elevated my NLP game significantly.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
What It’s About
Finally, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. This book is my gateway to machine learning without drowning in theory—perfect for jumping into projects.
Why I Love It
It’s intuitive, packed with examples, and uses tools I love (Scikit-Learn, Keras, TensorFlow). I didn’t need prior ML knowledge—just some coding skills—and I was off to the races.
What I Learned
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Basics to Advanced: Linear regression to deep neural networks.
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Unsupervised Learning: Clustering, anomaly detection—super useful.
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Neural Nets: CNNs, RNNs, GANs, and more.
Who It’s For
Beginners and pros alike—this book’s a keeper. It’s my go-to reference for any ML project.
Wrapping Up
These eight books have been my companions on this wild AI ride. They’ve taught me to code LLMs from scratch and create generative art. Now, I have the knowledge and confidence to build important applications. Here’s a quick recap:
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LLM Deep Dives: Build a Large Language Model (From Scratch), AI Engineering, and the LLM Engineer’s Handbook taught me a lot about LLMs and engineering.
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Broader AI has improved my skills. Here are some key resources:
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Generative AI for Beginners
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Generative Deep Learning
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Quick Start Guide to LLMs
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NLP with Transformers
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Hands-On Machine Learning
These have all added value to my toolkit.
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If you’re itching to start your own AI journey, grab one of these books (links below!) and let me know what you think. Have other recommendations? Drop them in the comments—I’m always eager to learn more. Thanks for joining me on this adventure—until next time.
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